[1]} gives nearly optimal confidence intervals for Gaussian mean estimation with finite sample
guarantees. [2]} proposes their own algorithms for the same problem and finds superior practical
performance in some domains, and [3]} develop an algorithm that works well at reasonably small sample sizes
and without strong assumptions on user knowledge,
while also scaling well to high dimensions.
[4]} explores non-parametric confidence intervals for calculating medians.
[5]} gives confidence intervals for a difference of means.
